气候变化领域集成服务门户(CCPortal): Estimating hourly and continuous ground-level PM2.5 concentrations using an ensemble learning algorithm: The ST-stacking model

CCPortal

DOI	10.1016/j.atmosenv.2019.117242
	Estimating hourly and continuous ground-level PM2.5 concentrations using an ensemble learning algorithm: The ST-stacking model
	Feng L.; Li Y.; Wang Y.; Du Q.
发表日期	2020
ISSN	1352-2310
卷号	223
英文摘要	Estimation of hourly and continuous ground-level fine particulate matter (PM2.5) concentrations is essential for PM2.5 pollution sources identifications, targeted policy development and population exposure research. However, current PM2.5 estimation studies rely heavily on satellite-based aerosol optical depth (AOD) data, and the limited transit times of polar-orbiting satellites such as Terra and Aqua, nighttime gaps in data from geostationary satellites such as Himawari-8, and cloud contamination reported for both types of satellites challenge the estimation of spatiotemporally continuous PM2.5 concentrations. In this study, spatiotemporal PM2.5 characteristic was constructed by the spatiotemporal fusion method. Specifically, multi-source data, including spatiotemporal, periodic, meteorological, vegetation, anthropogenic and topological characteristics, were incorporated into an ensemble learning method that combined extreme gradient boosting (XGBoost), k-nearest neighbour (KNN) and back-propagation neural network (BPNN) algorithms in level 1 and used linear regression (LR) for integration in level 2. The optimized stacking strategy that considered PM2.5 spatiotemporal autocorrelation was called the ST-stacking model. The model was trained, validated and tested with data acquired for China in 2017. The ST-stacking model outperformed XGBoost, KNN and BPNN models by 9.27% on average, with an R2 = 0.9191. Using the model, the 24-h and continuous ground-level PM2.5 concentrations in mainland China on 11 May 2017 were mapped, and parts of Beijing and Chengdu were selected for more detailed analysis. The PM2.5 concentrations in Taklimakan Desert, North China Plain, Sichuan Basin and Yangtze Plain were much higher than those in other locations on this day, which was generally consistent with the long-term patterns reported in previous studies. © 2019 Elsevier Ltd
关键词	China Hourly PM2.5 concentration PM2.5 mapping Spatiotemporal fusion Stacking strategy
语种	英语
scopus关键词	Backpropagation; Geostationary satellites; Nearest neighbor search; Orbits; Topology; Back-propagation neural networks; China; Fine particulate matter (PM2.5); K nearest neighbours (k-NN); PM2.5 concentration; Spatio-temporal fusions; Stacking strategy; Topological characteristics; Learning systems; algorithm; autocorrelation; concentration (composition); ensemble forecasting; optical depth; particulate matter; pollutant source; satellite altimetry; spatiotemporal analysis; algorithm; article; autocorrelation; back propagation neural network; China; desert; k nearest neighbor; learning algorithm; linear regression analysis; vegetation; Beijing [China]; Chengdu; China; North China Plain; Sichuan; Sichuan Basin; Taklimakan Desert; Xinjiang Uygur
来源期刊	ATMOSPHERIC ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/249372
作者单位	School of Resources and Environmental Science, Wuhan University, Wuhan, 430079, China; Key Laboratory of GIS, Ministry of Education, Wuhan University, Wuhan, 430079, China; Key Laboratory of Digital Mapping and Land Information Application Engineering, National Administration of Surveying, Mapping and Geoinformation, Wuhan University, Wuhan, 430079, China; Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan, 430079, China
推荐引用方式 GB/T 7714	Feng L.,Li Y.,Wang Y.,et al. Estimating hourly and continuous ground-level PM2.5 concentrations using an ensemble learning algorithm: The ST-stacking model[J],2020,223.
APA	Feng L.,Li Y.,Wang Y.,&Du Q..(2020).Estimating hourly and continuous ground-level PM2.5 concentrations using an ensemble learning algorithm: The ST-stacking model.ATMOSPHERIC ENVIRONMENT,223.
MLA	Feng L.,et al."Estimating hourly and continuous ground-level PM2.5 concentrations using an ensemble learning algorithm: The ST-stacking model".ATMOSPHERIC ENVIRONMENT 223(2020).